Method and system for detecting fraudulent bill payment transactions using dynamic multi-parameter predictive modeling

A method for detecting fraudulent bill payment transactions using dynamic multi-parameter predictive modeling, the method comprising:

• collecting historical bill payment transaction data representing bill payment transactions conducted through a bill payment system;
• obtaining historical fraudulent bill payment transactions data representing historical fraudulent bill payment transactions;
• processing the obtained historical fraudulent bill payment transactions data using one or more machine learning algorithms and identifying fraudulent bill payment parameters, the fraudulent bill payment parameters including rules evidencing common characteristics associated with individual fraudulent transactions of the historical bill payment transactions data;
• generate fraudulent bill payment transaction scoring parameters data based, at least in part, on the values of one or more interconnected fraudulent activity parameter/rule input variables identified in the historical fraudulent bill payment transactions data representing the identified fraudulent bill payment rules;
• using the fraudulent bill payment transaction scoring parameters data to create a fraudulent bill payment transaction scoring engine, the fraudulent bill payment transaction scoring engine assigning a fraudulent bill payment transaction score to provided current bill payment transaction data representing a current bill payment transaction, the fraudulent bill payment transaction score being based, at least in part, on analysis of one or more interconnected fraudulent bill payment parameters and/or the values of one or more interconnected fraudulent activity parameter/rule input variables identified in the current bill payment transaction and the one or more interconnected fraudulent bill payment parameters and/or the values of one or more interconnected fraudulent activity parameter/rule input variables identified in the historical fraudulent bill payment transactions data;
• defining one or more threshold fraudulent bill payment transaction score values;
• generating threshold fraudulent bill payment transaction score value data representing the one or more threshold fraudulent bill payment transaction score values;
• receiving current bill payment transaction data representing a current bill payment transaction being requested through the bill payment system by a user of the current bill payment transaction;
• before allowing the current bill payment transaction, providing the current bill payment transaction data to the fraudulent bill payment transaction scoring engine for analysis;
• determining a fraudulent bill payment transaction score value to be associated with the current bill payment transaction data, based, at least in part, on the analysis of the current bill payment transaction data by the fraudulent bill payment transaction scoring engine;
• and
• based, at least in part, on an analysis of the fraudulent bill payment transaction score value data associated with the current bill payment transaction data and the threshold fraudulent bill payment transaction score value data, taking one of the following actions:
• not allowing the current bill payment transaction to be completed;
• placing a hold on the current bill payment transaction by forwarding the current bill payment transaction data to a bill payment transaction inspection system and not forwarding and/or allowing the current bill payment transaction to the payee until the current bill payment transaction is authorized through the bill payment transaction inspection system;
• and
• allowing the current bill payment transaction be completed;
• updating, on a recurring basis, the historical fraudulent bill payment transactions data and recurringly processing the obtained historical fraudulent bill payment transactions data using one or more machine learning algorithms of a fraudulent activity predictive model and identifying one or more new fraudulent bill payment parameters, the fraudulent bill payment parameters including new rules evidencing common characteristics associated with individual fraudulent transactions of the updated historical bill payment transactions data and generating fraudulent bill payment transaction scoring parameters data representing the identified new fraudulent bill payment rules.

A method for detecting fraudulent bill payment transactions using dynamic multi-parameter predictive modeling comprising:

• providing a bill payment system using one or more computing systems;
• obtaining historic bill payment transaction data;
• identifying historical fraudulent bill payment transaction data in the historical bill payment transactions data;
• processing the historical fraudulent bill payment transactions data using one or more processors and one or more machine learning algorithms of an ensemble method model and/or a regression and classification fraudulent activity model;
• generating fraudulent bill payment transaction scoring parameters data based on the analysis of the historical fraudulent bill payment transactions data using the one or more processors and one or more machine learning algorithms of the ensemble method model and/or a regression and classification fraudulent activity model and identifying fraudulent bill payment parameters, the fraudulent bill payment parameters including rules evidencing common characteristics associated with individual fraudulent transactions of the historical bill payment transactions data;
• creating a fraudulent bill payment transaction scoring engine using the fraudulent bill payment transaction scoring parameters data;
• generating threshold fraudulent bill payment transaction score value data representing one or more defined threshold fraudulent bill payment transaction score values;
• receiving current bill payment transaction data representing a current bill payment transaction being requested through the bill payment system by a user of the current bill payment transaction;
• before allowing the current bill payment transaction to be conducted, providing the current bill payment transaction data to the fraudulent bill payment transaction scoring engine for analysis;
• based, at least in part, on the analysis of the current bill payment transaction data by the fraudulent bill payment transaction scoring engine determining and assigning fraudulent bill payment transaction score value data to the current bill payment transaction data;
• and
• based, at least in part, on an analysis of the fraudulent bill payment transaction score value data associated with the current bill payment transaction data and the threshold fraudulent bill payment transaction score value data, taking one of the following actions:
• preventing the current bill payment transaction represented by the current bill payment transaction data from being completed;
• placing a hold on the current bill payment transaction represented by the current bill payment transaction data;
• and
• allowing the current bill payment transaction represented by the current bill payment transaction data to be completed;
• updating, on a recurring basis, the historical fraudulent bill payment transactions data and recurringly processing the obtained historical fraudulent bill payment transactions data using one or more machine learning algorithms of a fraudulent activity predictive model and identifying one or more new fraudulent bill payment parameters, the fraudulent bill payment parameters including new rules evidencing common characteristics associated with individual fraudulent transactions of the updated historical bill payment transactions data and generating fraudulent bill payment transaction scoring parameters data representing the identified new fraudulent bill payment rules.